It is not easy to evaluate an algorithm whose task is to create
an understandable representation that matches the original incomprehensible
embedding. We performed a series of experiments, including a human study, that show that
our method indeed achieved its desired goal.
For all the experiments, we have used RoBERTa sentence embedding model as our \(f\)  [1]}, [2]} unless otherwise specified, which for simplicity we will now refer to as SRoBERTa
Model all-distilroberta-v1 from Hugging Face. Whenever the concept space \(C^*\)  was used, we set \(size=768\)  to match the size used by SRoBERTa, but we observed that using much smaller values yielded almost as good results.
For \(\tau \) ,
the function that maps concepts to text, we have just used the text of the concept name (with length of 4.25 words on average in \(G\) ).The total runtime for the experiments described here was 24 hours on 8 cores
of Intel Xeon Gold 5220R CPU 2.20GHz. The graph creation from the full
Wikipedia dump of 2020 took several days with maximal memory allocation of 100GB.
